Deep Learning Based Hand Wrist Crease Object Detection Models: An In-Depth Analysis

Abstract

Deep learning has evolved dramatically in recent years, resulting in a wide range of useful applications in various fields, such as computer vision, natural language processing, robotics, and autonomous vehicles. This expansion demonstrates the high transformative potential of deep learning techniques in real-world applications. This study addresses two critical challenges in this context. First, we recognized the unique potential of hand wrist creases as robust biometric identifiers for individuals. Second, we acknowledge the importance of custom deep learning models in enhancing the accuracy of object detection for hand wrist creases. In response, we propose a novel deep learning method for hand wrist crease object detection by leveraging, custom yolov8 models in this research. Our experiments involved comparing the performance of the custom Yolov8 with the Yolov5 and Yolov8 models. Notably, the custom Yolov8x model achieved an impressive object-detection accuracy of 97.1%.This innovative approach has the potential to significantly affect the development of biometric security systems, particularly in scenarios where traditional biometric identifiers are impractical or unreliable. By harnessing the power of deep learning and custom model architectures, this study paves the way for more accurate and reliable biometric identification systems, contributing to the advancement of security measures in a wide range of applications and domains.

Keywords

• Hand wrist crease
• object detection
• YOLO
• HWBID
• HWC-Yolov8